Natural Language Processing Challenges : Exam Practice Tests
Navigate the Depths of Natural Language Processing: Elevate Your Skills with Comprehensive Exam Practice Tests
4.63 (4 reviews)
4,775
students
614 questions
content
Feb 2024
last update
$19.99
regular price
What you will learn
Basic Concepts in NLP: Covering an introduction to natural language processing, its applications, and foundational terminology.
Tokenization and Text Preprocessing: Understanding tokenization techniques, stemming, lemmatization, and cleaning text data.
NLP Libraries and Tools: Exploring popular NLP libraries like NLTK, and spaCy, and their functionalities for text analysis.
Language Modeling: Introduction to language models, n-grams, and basic probabilistic models in NLP.
Sentiment Analysis: Basics of sentiment analysis, polarity detection, and simple sentiment classification techniques.
Named Entity Recognition (NER): Techniques for identifying and classifying named entities like names, locations, and organizations within text.
Word Embeddings: Introduction to word vectorization methods like Word2Vec, GloVe, and their applications in NLP tasks.
Part-of-Speech (POS) Tagging: Understanding the grammatical structure of sentences using POS tagging techniques.
Text Classification: Techniques for text categorization, including Naive Bayes, SVM, and neural network-based classifiers.
Topic Modeling: Exploring techniques like Latent Dirichlet Allocation (LDA) for extracting topics from text corpora.
Sequence-to-Sequence Models: Understanding advanced models like Recurrent Neural Networks (RNNs) and Transformer models for sequence-to-sequence tasks.
Language Generation: Techniques for text generation tasks like machine translation, summarization, and dialogue generation.
Ethical Considerations in NLP: Discussions on biases, fairness, and ethical challenges in NLP model development and deployment.
5752092
udemy ID
1/8/2024
course created date
1/17/2024
course indexed date
Bot
course submited by